attr): attr_val = _orig_module_getattr(mod, attr) return self.getattr(attr, attr_val, parameter_proxy_cache)# 定义对于`nn.Module`的forward方法的包装@functools.wraps(_orig_module_call)def module_call_wrapper(mod, *args, **kwarg...
attr_val, parameter_proxy_cache) # 定义对于`nn.Module`的forward方法的包装 @functools.wraps(...
第六个技巧是可以采用 torch.cuda.empty_cache() 来清理 GPU 缓存,这个方法在使用 notebook 的时候很有帮助,特别是你想要删除和重新创建一个很大的模型的时候。 使用例子如下所示: import gc example_model = ExampleModel().cuda() del example_model gc.collect() # The model will normally stay on the ...
In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward computation. You can cache arbitrary objects for use in the backward pass using the ctx.save_for_backward method....
"""@staticmethoddefforward(ctx, x):""" In the forward pass we receive a context object and a Tensor containing the input; we must return a Tensor containing the output, and we can use the context object to cache objects for use in the backward pass. ...
(ctx, input):"""In the forward pass we receive a Tensor containing the input and returna Tensor containing the output. ctx is a context object that can be usedto stash information for backward computation. You can cache arbitraryobjects for use in the backward pass using the ctx.save_for...
In the forward pass we receive a context object and a Tensor containing the input;we mustreturna Tensor containing the output,and we can use the context object to cache objectsforuseinthe backward pass.""" ctx.save_for_backward(x)returnx.clamp(min=0)defbackward(ctx,grad_output):""" ...
gradientswith# respect to these Tensors during the backward pass.w1=torch.randn(D_in,H,device=device,dtype=dtype,requires_grad=True)w2=torch.randn(H,D_out,device=device,dtype=dtype,requires_grad=True)learning_rate=1e-6fortinrange(500):# Forward pass:compute predicted y using operations on ...
def forward(ctx, input): """ In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward computation. You can cache arbitrary ...
return self.getattr(attr, attr_val, parameter_proxy_cache) # 定义对于`nn.Module`的forward方法的包装 @functools.wraps(_orig_module_call) def module_call_wrapper(mod, *args, **kwargs): def forward(*args, **kwargs): return _orig_module_call(mod, *args, **kwargs) ...